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luigicapogrosso/README.md

Hi there, I am Luigi 👋

🖥️ I am a Ph.D. candidate of the National Program in Artificial Intelligence at the University of Verona in collaboration with the Polytechnic of Turin in the IntelliGO labs under the supervision of Prof. Marco Cristani and Prof. Franco Fummi.

🎓 I received my B.Sc. (2019) and M.Sc. (2021) at the University of Verona in Computer Science and Computer Engineering for Robotics and Smart Industry, respectively.

📌 I had the privilege of being a visiting scholar at the Pattern Recognition and Automatic Learning Lab at Istituto Superior Técnico, headed by Prof. Mário A. T. Figueiredo.

📚 My research interests cover the broad area of deep learning, and the main topics I am currently working on can be grouped into the classes of efficient deep learning and representation learning.

📄 Check out all my publications on Google Scholar!

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  1. intelligolabs/MTL-Split intelligolabs/MTL-Split Public

    Official implementation of the paper "MTL-Split: Multi-Task Learning for Edge Devices using Split Computing" accepted @ DAC 2024.

    Python 3

  2. intelligolabs/LO-SC intelligolabs/LO-SC Public

    Official implementation of the paper "LO-SC: Local-only Split Computing for Accurate Deep Learning on Edge Devices" accepted @ VLSI Design 2025.

    Python 1 1

  3. intelligolabs/DIAG intelligolabs/DIAG Public

    Official implementation of the paper "Leveraging Latent Diffusion Models for Training-Free In-Distribution Data Augmentation for Surface Defect Detection" accepted @ CBMI 2024.

    Python 8

  4. intelligolabs/in_and_out intelligolabs/in_and_out Public

    Official implementation of the paper "Diffusion-based Image Generation for In-distribution Data Augmentation in Surface Defect Detection" accepted @ VISAPP 2024.

    Python 6 2

  5. HermesBDD HermesBDD Public

    Official implementation of the paper "HermesBDD: A Multi-Core and Multi-Platform Binary Decision Diagram Package" accepted @ DDECS 2023.

    C++ 6 3

  6. split_et_impera split_et_impera Public

    Official implementation of the paper "Split-Et-Impera: A Framework for the Design of Distributed Deep Learning Applications" accepted @ DDECS 2023.

    Python 5